• Aug 09, 2018 News! Vol. 6, No. 4-No. 7, No. 3 has been indexed by EI(Inspec)!   [Click]
  • Aug 09, 2018 News!Good News! All papers from Volume 8, Number 3 have been indexed by Scopus!   [Click]
  • May 23, 2018 News![CFP] 2018 the annual meeting of IJMLC Editorial Board, ACMLC 2018, will be held in Ho Chi Minh, Vietnam, December 7-9, 2018   [Click]
Search
General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(4): 394-398 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.4.718

Application of Remote Sensing Data for Dengue Outbreak Estimation Using Bayesian Network

Chanintorn Ruangudomsakul, Apinya Duangsin, Kittisak Kerdprasop, and Nittaya Kerdprasop
Abstract—Dengue is an epidemic that is a major endemic health problem found in many countries in tropical and worm area. Weather conditions are important factors directly influence the degree of dengue outbreak. Current practice in the dengue outbreak forecasting relies on the meteorological reports. This study shows an alternative way to estimate dengue outbreak level by using Bayesian network (BN). We employ the satellite based remote sensing data to generate the probability model that can estimate dengue outbreak level. We use publicly available satellite based remote sensing data from the NOAA STAR. The data consist of weekly SMN, SMT, VCI, VHI, TCI indexes as factors for estimating the dengue outbreak in the northeast region of Thailand. In this study, 3 BN models had been generated using expert knowledge, greedy thick thinning algorithm, and combination of expert and greedy thick thinning algorithm. All 3 models are validated with the 10-fold cross-validation and ROC Analysis. The experimental results on real data show that the model automatically generated by greedy tick tinning algorithm performs well on overall estimation of dengue outbreak levels. But for an abnormal situation that the outbreak level is significantly higher than usual, the BN model with combination of expert and greedy thick thinning algorithm perform the best in such situation.

Index Terms—Bayesian network, remote sensing data, dengue outbreak, epidemiology estimation model.

C. Ruangudomsakul is with the Department of Software Engineering, Faculty of Liberal Art and Science,Sisaket Rajabhat University. 319 Thai-panta Avenue, Muang District, Sisaket 33000, Thailand (e-mail: Chanintorn.r@sskru.ac.th).
A. Duangsin was with Epidemiology and Intelligence Group, Office of Disease Prevention and Control 10 Ubon Ratchathani 34000.
K. Kerdprasop and N. Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Thailand.

[PDF]

Cite: Chanintorn Ruangudomsakul, Apinya Duangsin, Kittisak Kerdprasop, and Nittaya Kerdprasop, "Application of Remote Sensing Data for Dengue Outbreak Estimation Using Bayesian Network," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 394-398, 2018.

Copyright © 2008-2018. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net